This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present principles that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present principles. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Resynchronization mechanisms for watermarking systems enable the watermark decoder to compute, from a potentially altered watermarked content, some information that has been used in the embedding stage to define the watermark carrier. This information is part of the underlying convention between the watermark embedder and detector upon which they relied to establish the watermarking communication channels. It is therefore required to re-compute the watermark carrier, and estimate the embedded payload. Without this information, or with erroneous information, the decoder cannot recover the payload, albeit said payload still being present within the content.
The popularity of 3D generated models has created the need for dedicated methods, designed to tackle robust watermarking of meshes. In the context of 3D watermarking, most watermarking systems rely on some robust primitive to compute the watermark carrier, such as the center of mass of the mesh as disclosed in “Triangle Surface Mesh Watermarking based on a Constrained Optimization Framework” (IEEE Transactions on Information Forensics and Security, 2014) by the same authors. Such a primitive is usually chosen to achieve a large robustness against potential attacks. For instance, when adding noise to the vertex positions, the center of mass is indeed a very stable reference primitive. However, its robustness against cropping wherein some portions of the mesh are removed and against a more general class of so-called “desynchronizing” operations which for instance adds noise only in parts of the mesh, is still an open problem.
The skilled in the art will appreciate that a non-blind watermarking system wherein the reference primitive is transmitted to the decoder, using for instance metadata, does not solve the resynchronization issue in 3D watermarking. Assuming that the decoder had knowledge of the position of the initial mesh center of mass, it would still not be able to compute the watermark carrier from a cropped and translated version of the watermarked mesh. The initial center of mass and the one that should be used to read the watermark from the attacked mesh would be offset because of the translation. Similarly, the center of mass estimated from the attacked mesh would also not coincide with the one that should be used due to the cropping operation. In other words, neither one of the two possible positions would be usable.
To address this problem, watermarking systems often rely upon a preprocessing stage, performed by both the embedder and the decoder, which results in a canonical partitioning of the mesh. For instance, in “Blind and Robust Watermarking of 3D Models: How to Withstand the Cropping Attack?” (IEEE International Conference on Image Processing, 2007), P. R. Alface et al. disclose a watermarking method that identifies feature points on the surface of a 3D mesh and defines a local neighborhood from them. This can be viewed as a partition of the 3D surface where a plurality of local neighborhoods is watermarked. Based on this partitioning, the watermark information is repeatedly embedded in each local neighborhood, thereby creating a redundancy of the payload in subparts of the mesh. Individual local neighborhoods are generally less affected by the aforementioned desynchronizing operations than a global watermark carrier such as the location of the center of mass. For instance, in the case of a cropping attack, each local neighborhood is often either lost (cropped out), or left unaltered. Thus, the watermark decoder can identify and use the remaining local neighborhoods to recover the watermark payload.
Watermarking systems relying on such partitioning still exhibit robustness issues. Firstly, such watermarking system assumes that the watermark detector is able to re-compute the same canonical partitioning as the embedder. However, this proves to be difficult in practice. Secondly, the watermark payload is no longer embedded in the whole mesh, but repeated in multiple sub-regions. This may decrease the overall robustness and/or the amount of embedded watermark information.
Thus, watermarking schemes based on stable reference primitive seem preferable and a synchronization mechanism that allows a reference primitive used during watermark embedding, possibly altered due to modifications to the cover 3D mesh (e.g. rotation, translation, scaling, cropping), to be recovered for watermark detection is therefore needed.
Outside the field of watermarking, methods for detecting features point on a 3D object have been studied. For instance, I. Sipiran and B. Bustos in “Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes” (in The Visual Computer: International Journal of Computer Graphics, vol. 27, no. 11, pp. 963-976, 2011) present a detector of points of interest for 3D objects based on Harris operator. To that end, a quadratic surface is fitted on the local neighborhood around the feature point. Advantageously, the characteristics of the fitted surface are invariant to rigid transforms or scaling and allow recovering the feature point. However, Sipiran is silent on the recovery of stable reference primitive, such as a center of mass, from the features points.